24/7 Dremio Support
Keep Your Lakehouse at
Peak Performance.
We are Open source Code Contributor
Zero-Day Vulnerability Fixes
Critical Vulnerability Assessment
Roadmap & Recommendations
SLA-Backed Technical Support
Zero-Day Vulnerability Fixes
Critical Vulnerability Assessment
Roadmap & Recommendations
SLA-Backed Technical Support
Dremio Support That's Built to Meet the World's Strictest Data Lakehouse Standards
En(AI)blingTM Success for Industry Leaders
Dremio Support Packages
Whether you run a single Dremio Software deployment or a large-scale distributed data lakehouse environment on Kubernetes, AWS, Azure, or GCP, our Dremio enterprise support plans are designed around your operational needs.
Standard
Advanced
Platinum
Delivering Measurable Outcomes for Dremio-Driven Businesses
Organizations across finance, healthcare, logistics, and media trust Ksolves to optimize and support mission-critical Dremio data lakehouse environments with enterprise-grade support.
99.99%
SLA Maintained
SLA Maintained
Ksolves holds 99.99% uptime across client environments through proactive monitoring, auto-healing pipelines, and zero-drama incident response.
40%
Lower TCO
Lower TCO
From licensing audits to compute consolidation, Ksolves cuts total cost of ownership by 40%, without cutting corners on performance or reliability.
98%
Contract Renewal Rate
Contract Renewal Rate
We take pride in saying 98% of clients come back. Not because of lock-in, but because the work speaks for itself. That’s Ksolves Promise - on time, on budget, and exactly what was promised.
30 Min
Turnaround Time
Turnaround Time
Ksolves responds and resolves in under 30 minutes, keeping production running and teams unblocked.
Dremio Support Services for Your Complete Data Lakehouse Lifecycle
From deploying Dremio clusters and configuring Reflections to 24×7 incident response and version upgrades, we manage every stage of your Dremio lifecycle.
24×7 Cluster Operations, Fully Managed
Our engineers manage and optimize your Dremio deployment 24×7 so your data teams focus on analytics, not infrastructure.
- Cluster installation on bare metal, Kubernetes (Helm), Amazon EC2, Azure AKS, and Google GKE
- Coordinator and Executor node configuration via coordinator.enabled and executor.enabled with embedded or external ZooKeeper support
- Workload Management (WLM) queue design, UI Previews, Low Cost User Queries, High Cost User Queries, Low Cost Reflections, and High Cost Reflections queues with per-queue concurrency limits and routing rules
- Automated backup, disaster recovery, and monthly cluster health and performance reporting
Real-Time Visibility into Reflection Health and Query Performance
We deploy Dremio's full observability layer, detecting Reflection failures and Coordinator instability before your stakeholders notice.
- Query performance dashboards via Prometheus, Grafana, and Dremio's built-in query profile viewer
- Reflection refresh failure and jobs.queue.<queue_name>.waiting threshold alerting via PagerDuty, Slack, and OpsGenie
- Coordinator health monitoring, metadata refresh latency, job submission delays, and queue depth tracking via the Dremio metrics endpoint
- Stuck job detection and automated cancellation via POST /api/v3/job/{jobId}/cancel
Compliance-Ready Security at Every Layer
We apply defence-in-depth across authentication, secrets management, and access control for GDPR, HIPAA, PCI-DSS, and SOC 2 environments.
- RBAC with role-scoped Space, Folder, and dataset-level access policies
- LDAP, OAuth2, Okta, and SAML SSO integration for enterprise identity providers
- Column-level masking and row-level access policies across Physical and Virtual Datasets
- Secrets backend integration with HashiCorp Vault and AWS Secrets Manager; TLS across web interface and Arrow Flight endpoints
Deep-Layer Dremio Performance Engineering
We fix performance at the Coordinator, Executor, Reflection, and semantic layer, resolving root causes, not symptoms.
- Executor memory tuning via DREMIO_MAX_HEAP_MEMORY_SIZE_MB, DREMIO_MAX_DIRECT_MEMORY_SIZE_MB, and services.coordinator.scheduler.threads
- WLM queue tuning, maxAllowedRunningJobs, maxQueryMemoryPerNodeBytes, and maxStartTimeoutMs configured via the Admin UI or WLM REST API
- Reflection design review: field selection, incremental refresh configuration, and Reflection recommendation evaluation
- Virtual Dataset refactoring to eliminate nested view chains and Iceberg OPTIMIZE and VACUUM scheduling to reduce small-file overhead
Zero-Downtime Dremio Version Upgrades
We manage Dremio version migrations with zero downtime, covering Helm chart upgrades, metadata store migrations, and connector transitions.
- Pre-upgrade Virtual Dataset and source connector compatibility audit with deprecated API identification
- Helm chart migration with PVC reconfiguration, ConfigMap updates, and StatefulSet changes between major versions
- Metadata store upgrade with rollback plan and staging environment validation before production cutover
- Post-upgrade Iceberg compatibility verification, Reflection rebuild validation, and formal sign-off
Incident Resolution and Root Cause Analysis
We trace every production Dremio incident to the actual root, close it fast, and document it so it never recurs.
- Emergency triage for Coordinator failures, Reflection breakdown cascades, and Executor node loss
- Source connectivity error diagnosis: Arrow Flight failures, JDBC/ODBC driver conflicts, and metadata refresh timeouts
- Reflection materialization failure investigation and orphan cleanup via dremio-admin clean --delete-orphans
- Kubernetes OOM kill root cause identification via Executor heap and direct memory diagnostics with resource quota remediation
Through the Client's Lens
Why Ksolves is a Trusted Choice of Global Teams for Dremio Support?
Backed by certified expertise and SLA-driven support, Ksolves helps enterprises maximize Dremio data lakehouse performance without operational risk.
90%
Client Retention Rate
750+
Projects Successfully
Delivered
NSE & BSE
Publicly Listed
Company
600+
Workforce and still
growing
350+
Certifications
200+
Happy Clients
150K+
Support Hours
Completed
Industries We Help Scale with Dremio
From real-time self-service analytics to ML feature data preparation, Ksolves is a trusted Dremio managed service provider helping industries run lakehouse queries with maximum performance, reliability, and uptime.
Telecom
We manage real-time telecom data lakehouse environments, handling network telemetry ingestion, CDR query acceleration via Reflections, and Dremio cluster monitoring at carrier scale.
Healthcare
We manage HIPAA-compliant Dremio environments with HL7 and FHIR Virtual Datasets, patient data query pipelines, and audit-ready column-level security and row-level access policies across clinical systems.
E-Commerce
We keep inventory datasets, order pipelines, and customer behaviour Virtual Datasets in sync across every fulfilment channel via Dremio Reflections and Apache Iceberg table optimization.
Fintech
We support Dremio environments built for transaction query acceleration, fraud detection data flows, and regulatory reporting, where every lakehouse query has a direct financial consequence.
Entertainment
We support high-throughput Dremio environments for content metadata Virtual Datasets, user engagement query acceleration, and recommendation feed data flows that scale with audience demand.
Manufacturing
We connect shop floor sensor feeds, MES system tables, and supply chain datasets into unified Dremio semantic layers that keep operations running without interruption.
Retail
We manage Dremio environments connecting POS system sources, loyalty platform datasets, and customer data across physical and digital channels into real-time self-service analytics layers.
Banking & Financial Services
We support banking lakehouse environments built for transaction query processing and regulatory reporting across multiple jurisdictions with full RBAC and audit logging.
Logistics & Supply Chain
We manage Dremio environments covering shipment tracking datasets, warehouse telemetry, carrier source integrations, and last-mile data flows across distribution networks.
Technology & SaaS / Cloud
We support Dremio deployments, routing data across internal sources, third-party APIs, and multi-tenant cloud-native infrastructure on AWS, Azure, and GCP without disruption.
Ksolves: Insights from Enterprise Experts
Explore the latest real-time data processing trends, stream processing strategies, and expert insights for building scalable, reliable, and high-performance data environments.
Success Stories from Global Enterprises
Ksolves Big Data Experts have delivered excellence for multiple clients operating across industries. Explore the case studies and experience the Ksolves Impact.
Multi-Site CDR Pipeline for a Telecom Operator Across 4 Remote Locations
Challenge
CDR data from 4 remote sites had no unified ingestion- billing reconciliation was fully manual, causing revenue leakage as subscriber volumes grew.
Solution
NiFi agents at all 5 sites feed Kafka → Spark → Druid, with live Superset dashboards for billing and network teams.
Sub-second
Query Response on Live CDR Data
NiFi 1.27 → 2.7 Kubernetes Migration- Financial Services
Challenge
NiFi 1.27 is running on bare metal with no SSO, no scalability, and a growing compliance pipeline that the architecture couldn't support.
Solution
Migrated to NiFi 2.7 on Kubernetes with OneLogin SSO integration, zero downtime, completed in 6 weeks.
3X
Scalability Headroom - 6 Weeks, Zero Downtime
Eliminating ~900K Duplicate Oil Well Records via Azure Databricks
Challenge
The same wellbore appeared under 3–4 different IDs across 6,200 Excel files and 8 systems, causing royalty errors and a BLM audit risk.
Solution
Azure Databricks + PySpark deduplication with geospatial blocking and an ML model (F1=0.971), plus a human-in-the-loop MDM review portal.
~900K
Duplicate Records Eliminated
Petabyte CDR Migration from MapR to ClickHouse -Zero Data Loss
Challenge
Years of CDR data on an end-of-life MapR platform with no vendor support. Compliance queries took 4–6 hours, and regulators required signed proof of zero data loss.
Solution
Spark migrated data in resumable batches with 4 automated validation checks per batch. NiFi produced a signed migration certificate. ClickHouse was optimised for compliance queries from day one.
<8s
Compliance Query Time (from 4–6 hours)
AI-Ready Open Lakehouse on Red Hat OpenShift- Gulf Retailer
Challenge
SAP S/4HANA was too expensive. Cloud platforms unavailable across GCC. 16 TB of daily data needed sub-second processing, and Power BI reports couldn't be touched.
Solution
On-premises lakehouse on existing OpenShift: NiFi → Kafka → Flink → Iceberg on MinIO → Trino serving Power BI as a drop-in SAP BW replacement. Zero new hardware.
16 TB
Daily Data: Sub-Second SLA, Zero New Hardware
Multi-Site CDR Pipeline for a Telecom Operator Across 4 Remote Locations
Challenge
CDR data from 4 remote sites had no unified ingestion- billing reconciliation was fully manual, causing revenue leakage as subscriber volumes grew.
Solution
NiFi agents at all 5 sites feed Kafka → Spark → Druid, with live Superset dashboards for billing and network teams.
Sub-second
Query Response on Live CDR Data
NiFi 1.27 → 2.7 Kubernetes Migration- Financial Services
Challenge
NiFi 1.27 is running on bare metal with no SSO, no scalability, and a growing compliance pipeline that the architecture couldn't support.
Solution
Migrated to NiFi 2.7 on Kubernetes with OneLogin SSO integration, zero downtime, completed in 6 weeks.
3X
Scalability Headroom - 6 Weeks, Zero Downtime
Eliminating ~900K Duplicate Oil Well Records via Azure Databricks
Challenge
The same wellbore appeared under 3–4 different IDs across 6,200 Excel files and 8 systems, causing royalty errors and a BLM audit risk.
Solution
Azure Databricks + PySpark deduplication with geospatial blocking and an ML model (F1=0.971), plus a human-in-the-loop MDM review portal.
~900K
Duplicate Records Eliminated
Petabyte CDR Migration from MapR to ClickHouse -Zero Data Loss
Challenge
Years of CDR data on an end-of-life MapR platform with no vendor support. Compliance queries took 4–6 hours, and regulators required signed proof of zero data loss.
Solution
Spark migrated data in resumable batches with 4 automated validation checks per batch. NiFi produced a signed migration certificate. ClickHouse was optimised for compliance queries from day one.
<8s
Compliance Query Time (from 4–6 hours)
AI-Ready Open Lakehouse on Red Hat OpenShift- Gulf Retailer
Challenge
SAP S/4HANA was too expensive. Cloud platforms unavailable across GCC. 16 TB of daily data needed sub-second processing, and Power BI reports couldn't be touched.
Solution
On-premises lakehouse on existing OpenShift: NiFi → Kafka → Flink → Iceberg on MinIO → Trino serving Power BI as a drop-in SAP BW replacement. Zero new hardware.
16 TB
Daily Data: Sub-Second SLA, Zero New Hardware
Frequently Asked Questions
Everything you need to know before choosing a Dremio support partner.
Cluster installation on Kubernetes, AWS, Azure, and GCP; Coordinator and Executor configuration; Reflection design and optimization; monitoring and alerting; secrets management; version upgrades; and 24/7 incident response for production query failures and Reflection breakdowns.
The most common causes are Reflections not matching queries due to stale refresh cycles or schema drift, excessive full dataset scans from missing Iceberg partitioning, metadata store bloat from unarchived job history, and Executor OOM kills from undersized DREMIO_MAX_HEAP_MEMORY_SIZE_MB. Ksolves diagnoses and resolves all of these as part of its performance tuning service.
We support all major deployment models: bare metal, Docker Compose, Kubernetes (Helm chart), Amazon EC2, Azure AKS, Google GKE, and Dremio Cloud. Our approach is tailored to your infrastructure, Executor configuration, and cloud storage layer, S3, ADLS, or GCS.
Critical issue acknowledgement within 30 minutes and resolution within 1–4 hours, depending on plan tier. All SLAs are contractually backed, tracked in monthly reports, and include a dedicated escalation path in the Platinum tier.
Dremio provides native read and write support for Iceberg, including INSERT, UPDATE, DELETE, MERGE, time travel, schema evolution, and partition pruning. Table maintenance is handled via OPTIMIZE and VACUUM commands to manage file sizes and remove expired snapshots.
A Physical Dataset (PDS) is a direct representation of an underlying source, a file, folder, or table registered in Dremio. A Virtual Dataset (VDS) is a saved SQL query layered on top of PDSs or other VDSs, forming the semantic layer for reusable, governed data definitions without duplicating data.
The most common causes are schema changes in the source that break the Reflection definition, source connectivity failures during the refresh window, insufficient Executor memory for large materializations, and expired source credentials. Ksolves monitors Reflection health continuously and resolves failures before they impact query performance.
Dremio connects via ODBC, JDBC, and Arrow Flight SQL drivers. Arrow Flight SQL delivers significantly faster data transfer than traditional JDBC/ODBC using Apache Arrow’s columnar format over gRPC, enabling direct secure connectivity from Power BI, Tableau, Looker, and Excel without a middleware layer.
Dremio Sonar is the SQL query engine providing Reflection-based acceleration, Virtual Dataset management, and BI connectivity. Dremio Cloud Catalog is the managed Apache Iceberg catalog powered by Project Nessie, offering Git-like branch-based versioning, isolated schema changes, and multi-table ACID transactions across the lakehouse.
